code: def train(
      network_def,
      online_params,
      optimizer,
      optimizer_state,
      states,
      actions,
      returns_to_condition,
      support,
  ):
      """Run a training step."""
  
      def loss_fn(params):
          """Computes the distributional loss for C51 or huber loss for DQN."""
  
          def q_func(state, return_to_condition):
              return network_def.apply(
                  params,
                  state,
                  support=support,
                  return_to_condition=return_to_condition,
              )
  
          q_values = get_q_values(q_func, states, returns_to_condition)
          replay_chosen_q = jax.vmap(lambda x, y: x[y])(
              q_values, actions
          )
          bc_loss = jnp.mean(
              jax.scipy.special.logsumexp(q_values, axis=-1)
              - replay_chosen_q
          )
          return bc_loss
  
      grad_fn = jax.value_and_grad(loss_fn, has_aux=False)
      # Get the unweighted loss without taking its mean for updating priorities.
      # outputs[1] correspond to the per-example TD loss.
      loss, grad = grad_fn(online_params)
      updates, optimizer_state = optimizer.update(
          grad, optimizer_state, params=online_params
      )
      online_params = optax.apply_updates(online_params, updates)
      return optimizer_state, online_params, loss
  
imports: import functools
  from absl import logging
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.jax.agents.rainbow import rainbow_agent
  from dopamine.labs.offline_rl.jax import offline_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: train(network_def, online_params, optimizer, optimizer_state, states, actions, returns_to_condition, support) and returns: optimizer_state,  online_params,  loss, bc_loss, network_def